Timothy L. Tickle

This paper introduces a service for automated cell type annotation in scRNA-seq data using reverse search against a curated database, providing both fine-grained and coarse annotations without relying on predefined markers.

The paper constructs a single-cell reference atlas for human and mouse lung tissues to improve cell type annotation and data integration.

FusionInspector identifies and characterizes biologically relevant fusion transcripts from RNA-seq data in tumor and normal tissues using clustering and machine learning.

The paper introduces MAMS, a metadata standard for single-cell data matrices and analysis workflows, to improve reproducibility and interoperability across datasets.

The paper describes the LungMAP Portal Ecosystem, a centralized resource integrating multi-omics and imaging data across species to enable standardized analysis of lung biology and disease.

The paper describes the Neuroscience Multi-Omic Archive, a repository for single-cell transcriptomic and epigenomic data from mammalian brains, providing tools for data access and analysis.

The paper provides a guide to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem, its tools, and resources for analyzing brain cell types across species.

The paper generates a multimodal cell census and atlas of the mammalian primary motor cortex, revealing a unified molecular genetic landscape and cross-species taxonomy of cell types.

This study uses single-cell RNA-sequencing to analyze fetal bone marrow development and identifies disruptions in hematopoiesis in Down syndrome.

The paper introduces a statistical model to simulate microbial community profiles and evaluates its performance in benchmarking analysis methods.

This study creates a single-cell and spatial atlas of autopsy tissues from COVID-19 patients, revealing altered cellular responses and potential therapeutic targets.

Cumulus is a cloud-based framework for analyzing large-scale sc/snRNA-seq data, improving efficiency and scalability while maintaining result quality.

The paper develops a toolbox for scRNA-Seq and snRNA-Seq of fresh and frozen human tumors, testing it across eight tumor types and optimizing protocols for different tissue characteristics.

The paper uses single-cell transcriptomics to map hematopoiesis in human fetal liver, identifying cell differentiation trajectories and shifts in hematopoietic composition during development.

Filtering STAR's chimeric alignments against gene annotations outscored 15 other fusion callers on 60 cancer cell lines and ran fastest, averaging 2.3 single-core hours per sample.